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@ -48,6 +48,19 @@
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<meta property="og:title" content="">
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<meta property="og:site_name" content="">
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<meta property="og:url" content="">
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<meta property="og:description" content="Transform your prototype AI code into production-ready software.">
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<meta property="og:type" content="">
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<meta property="og:image" content=https://great-ai.scoutinscience.com/media/og-image.png>
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<style>
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.jupyter-wrapper a {
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color: var(--md-typeset-a-color) !important;
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}
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</style>
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</head>
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@ -353,6 +366,20 @@
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<li class="md-nav__item">
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<a href="../install/" class="md-nav__link">
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Installation guide
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</a>
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</li>
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<li class="md-nav__item">
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<a href="../create-service/" class="md-nav__link">
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How to create a GreatAI service
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@ -705,7 +732,7 @@
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<li class="md-nav__item">
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<a href="../../examples/simple/data/" class="md-nav__link">
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Train a domain classifier on the semantic scholar dataset
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Simple example: data engineering
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</a>
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</li>
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@ -719,7 +746,7 @@
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<li class="md-nav__item">
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<a href="../../examples/simple/train/" class="md-nav__link">
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Train a domain classifier on the semantic scholar dataset
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Optimise and train a model
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</a>
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</li>
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@ -733,7 +760,7 @@
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<li class="md-nav__item">
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<a href="../../examples/simple/deploy/" class="md-nav__link">
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Train a domain classifier on the semantic scholar dataset
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Hardening and deployment
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</a>
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</li>
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@ -876,6 +903,7 @@
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<a href="https://github.com/schmelczer/great-ai/edit/main/docs/how-to-guides/use-service.md" title="Edit this page" class="md-content__button md-icon">
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<svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M20.71 7.04c.39-.39.39-1.04 0-1.41l-2.34-2.34c-.37-.39-1.02-.39-1.41 0l-1.84 1.83 3.75 3.75M3 17.25V21h3.75L17.81 9.93l-3.75-3.75L3 17.25Z"/></svg>
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@ -919,9 +947,10 @@
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<p>Some configuration options are also supported.</p>
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<div class="highlight"><pre><span></span><code><a id="__codelineno-3-1" name="__codelineno-3-1" href="#__codelineno-3-1"></a>great-ai greeter.py --port <span class="m">8000</span> --host <span class="m">127</span>.0.0.1 --timeout_keep_alive <span class="m">10</span>
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</code></pre></div>
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<blockquote>
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<p>For more options (but no Notebook support, use <a href="https://www.uvicorn.org/" target="_blank">uvicorn</a>)</p>
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</blockquote>
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<details class="note">
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<summary>More options</summary>
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<p>For more options (but no Notebook support), simply use <a href="https://www.uvicorn.org/" target="_blank">uvicorn</a> for starting your app (available at <code>greeter.app</code>).</p>
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</details>
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<h3 id="in-production">In production<a class="headerlink" href="#in-production" title="Permanent link">#</a></h3>
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<p>There are three main approaches for deploying a GreatAI service.</p>
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<h4 id="manual-deployment">Manual deployment<a class="headerlink" href="#manual-deployment" title="Permanent link">#</a></h4>
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@ -941,7 +970,7 @@
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<p>You can replace <code>pwd</code> with the path to your code's folder.</p>
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</blockquote>
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<h4 id="use-a-platform-as-a-service">Use a Platform-as-a-Service<a class="headerlink" href="#use-a-platform-as-a-service" title="Permanent link">#</a></h4>
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<p>Similarly to the previous approach, your code will run in a container. However, instead of manually managing it, you can just choose from a plethora of PaaS providers (such as <a href="https://www.digitalocean.com/products/app-platform" target="_blank">DO App platform</a> or <a href="https://mlem.ai/" target="_blank">MLEM</a>) that take a Docker image as a source and handle the rest of the deployment.</p>
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<p>Similarly to the previous approach, your code will run in a container. However, instead of manually managing it, you can just choose from a plethora of PaaS providers (such as <a href="https://aws.amazon.com/ecs/" target="_blank">AWS ECS</a>, <a href="https://www.digitalocean.com/products/app-platform" target="_blank">DO App platform</a>, <a href="https://mlem.ai/" target="_blank">MLEM</a>) that take a Docker image as a source and handle the rest of the deployment.</p>
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<p>To this end, you can also create a custom Docker image. It is especially useful if you have third-party dependencies, such as <a href="https://pytorch.org/" target="_blank">PyTorch</a> or <a href="https://www.tensorflow.org/" target="_blank">TensorFlow</a>.</p>
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<div class="highlight"><pre><span></span><code><a id="__codelineno-6-1" name="__codelineno-6-1" href="#__codelineno-6-1"></a><span class="k">FROM</span><span class="w"> </span><span class="s">schmelczera/great-ai:latest</span>
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<a id="__codelineno-6-2" name="__codelineno-6-2" href="#__codelineno-6-2"></a>
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@ -988,7 +1017,7 @@
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<small>
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Last update:
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<span class="git-revision-date-localized-plugin git-revision-date-localized-plugin-date">July 11, 2022</span>
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<span class="git-revision-date-localized-plugin git-revision-date-localized-plugin-date">July 12, 2022</span>
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</small>
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@ -51,7 +51,8 @@ Some configuration options are also supported.
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```sh
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great-ai greeter.py --port 8000 --host 127.0.0.1 --timeout_keep_alive 10
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```
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> For more options (but no Notebook support, use [uvicorn](https://www.uvicorn.org/){ target=_blank })
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??? note "More options"
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For more options (but no Notebook support), simply use [uvicorn](https://www.uvicorn.org/){ target=_blank } for starting your app (available at `greeter.app`).
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### In production
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@ -80,7 +81,7 @@ docker run -p 6060:6060 --volume `pwd`:/app --rm \
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#### Use a Platform-as-a-Service
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Similarly to the previous approach, your code will run in a container. However, instead of manually managing it, you can just choose from a plethora of PaaS providers (such as [DO App platform](https://www.digitalocean.com/products/app-platform){ target=_blank } or [MLEM](https://mlem.ai/){ target=_blank }) that take a Docker image as a source and handle the rest of the deployment.
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Similarly to the previous approach, your code will run in a container. However, instead of manually managing it, you can just choose from a plethora of PaaS providers (such as [AWS ECS](https://aws.amazon.com/ecs/){ target=_blank }, [DO App platform](https://www.digitalocean.com/products/app-platform){ target=_blank }, [MLEM](https://mlem.ai/){ target=_blank }) that take a Docker image as a source and handle the rest of the deployment.
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To this end, you can also create a custom Docker image. It is especially useful if you have third-party dependencies, such as [PyTorch](https://pytorch.org/){ target=_blank } or [TensorFlow](https://www.tensorflow.org/){ target=_blank }.
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